Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

东方坳隧道支护电磁波法无损检测应用研究

介绍隧道支护原理及探地雷达探测原理,结合某公路隧道工程,分析探讨隧道无损检测的具体应用。介绍工程测线布置,从喷混厚度、拱架间距、喷混与围岩的接触情况分析初支检查结果;从空洞及不密实缺陷、钢筋数量及间距分析二衬检测结果。

基于谱峰个数的无人机与武装单兵分类识别算法

本文基于无人机与武装单兵分类识别问题,提出了基于多普勒谱峰个数的目标识别算法,该算法用于战场环境侦察雷达快速准确地鉴别无人机与武装单兵。本文解决了低空无人机与地面武装单兵难以区分的问题;算法中充分利用了无人机旋翼转动与武装单兵四肢摆动在多普勒谱上的差异,提出了多普勒谱峰个数特征来进行分类识别。解决了传统方法难以识别无人机与武装单兵的问题。

基于时频域特征融合的IR-UWB穿墙雷达人体行为识别方法

冲激脉冲(impulse radio,IR)超宽带(ultra-wideband,UWB)穿墙雷达因其良好的穿透性和距离分辨率在穿墙人体行为识别领域具有重要作用,但是常规识别方法仅采用单域特征对行为模式进行描述,识别准确率不高。针对这一问题,提出基于时频域特征融合的IR-UWB穿墙雷达人体行为识别算法。首先,通过杂波抑制及距离补偿方法获取高信噪比的人体行为距离像。其次,基于距离像提取目标时域特征,与频域特征进行融合,构建数据集。最后,基于支持向量机(support vector machine,SVM)算法对人体行为进行识别。实验结果表明,所提算法对于IR-UWB穿墙雷达人体行为识别能够达到95%的准确率。

两级多层系统架构下的情报生成系统研究

在多维复杂信息化作战场景和国产化软硬件环境的条件下,针对雷达组网军用信息系统领域中情报生成基础数据庞大、牵涉流程复杂、即时性差等问题,设计两级多层系统软件架构下的情报生成系统软件,自顶向下对系统进行层次化分解,将系统组件分隔到不同的层中,实现了前后台分离解耦,提升了国产化软硬件环境下系统软件使用潜力。以知识库为核心,以海量数据提取和预案匹配服务为手段,根据数据关联、特征矩阵、要素提取、指标体系构建情报生成系统知识库。提供情报要素可视化在线编辑、情报模板智能生成技术手段,提升情报产品的高可配置和灵活性,基于wps二次开发技术,快速生成指挥员关注情报产品,实现情报产品快速整编,兼顾一定的辅助决策能力。

面向用户的机载有源相控阵雷达保障体系

目前,我国主流战机均装备了有源相控阵雷达。首先,介绍了雷达装备的可靠性和可维修性;然后,针对该型号雷达技术性能先进、结构复杂、装备数量多且装备用户分散等保障难点,建立了一种面向用户的机载有源相控阵雷达保障体系,包括雷达自保障体系和工厂保障体系;最后,对雷达自保障体系中机载雷达健康管理系统和工厂保障体系中的保障资源进行了讨论,对于提高有源相控阵雷达的战备完好性、节省寿命周期费用具有重要的意义。

Effect of biased estimation on radar-to-ESM track association

The track association problem of radar and electronic support measure （ESM） has been considered in the literature for several years. This problem is crucial for radar-to-ESM track fusion and is complicated by the presence of individual systematic errors and measurement errors. In order to improve the track association of radar and ESM sensors, a pseudo-linear filtering algorithm is proposed to estimate the target states and improve the stability of the filter. It is found that, however, the correct probability of radar- to-ESM track association decreases as the radar measurement error decreases, when the pseudo-linear filter is used for ESM sensor filtering. In view of the strange phenomenon, this paper analyzes the reason for it by using the statistic theory and further performs Monte Carlo simulation to verify the analysis.

Application of wavelet support vector regression on SAR data de-noising

A new filtering method for SAR data de-noising using wavelet support vector regression （WSVR） is developed. On the basis of the grey scale distribution character of SAR imagery, the logarithmic SAR image as a noise polluted signal is taken and the noise model assumption in logarithmic domain with Gaussian noise and impact noise is proposed. Based on the better per- formance of support vector regression （SVR） for complex signal approximation and the wavelet for signal detail expression, the wavelet kernel function is chosen as support vector kernel func- tion. Then the logarithmic SAR image is regressed with WSVR. Furthermore the regression distance is used as a judgment index of the noise type. According to the judgment of noise type every pixel can be adaptively de-noised with different filters. Through an approximation experiment for a one-dimensional complex signal, the feasibility of SAR data regression based on WSVR is con- firmed. Afterward the SAR image is treated as a two-dimensional continuous signal and filtered by an SVR with wavelet kernel function. The results show that the method proposed here reduces the radar speckle noise effectively while maintaining edge features and details well.

A NOVEL SVM FOR GROUND PENETRATING SYNTHETIC APERTURE RADAR LANDMINE DETECTION

The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.

Radar high resolution range profile recognition via multi-SV method

For radar high resolution range profile (HRRP) recognition, three aspects are of great importance to improve the performance, i.e. discrimination for outlier, classification for inner and an accurate description for feature space. To tackle these issues, a novel target recognition method is designed, denoted by the multiple support vectors (multi-SV) method. With the proposed method, a special framework is constructed by a treble correlate support vector model to segment the feature space to two regions with the distribution of density, and then the description and classification hyperplane for each region are achieved. Based on the support vector framework, this method needs less memory and computation complexity to fit practical radar HRRP recognition. Finally, the experiment based on the measured data verifies the excellent performance of this method.

Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images

Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.

Oil spill detection by a support vector machine based on polarization decomposition characteristics

Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,remote sensing is one of the major approaches for monitoring the oil spill.Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy（H） and reflection entropy（A）.The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill.The findings show that the information extracted from（1-A）×（1-H） and（1-H）×A parameters is relatively evident effects.The results of extraction of the oil spill information based on H×A parameter are relatively not good.The combination of the two has something to do with H and A values.In general,when H〉0.7,A value is relatively small.Here,the extraction of the oil spill information using（1-A）×（1-H） and（1-H）×A parameters obtains evident effects.Whichever combined parameter is adopted,oil well data would cause certain false alarm to the extraction of the oil spill information.In particular the false alarm of the extracted oil spill information based on（1-A）×（1-H） is relatively high,while the false alarm based on（1-A）×H and（1-H）×A parameters is relatively small,but an image noise is relatively big.The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.

BIM 技术在地下管廊与市政配套工程中应用价值分析

市政领域需要协调的专业很广,无论是市政与路桥、景观与道路、地下管线与地下管廊,可以说,BIM技术可以贯穿设计、施工、运维的全过程,为多专业协同工作提供了可能。本文按照设计到竣工的全过程为时间线,将探索的技术应用点串联成伴随全过程的工作流程,由浅入深地阐述BIM技术在地下管廊与市政配套工程中的应用价值,为建筑业的产业升级提供一定的技术支持与数据支撑。

Plastic zone analysis and support optimization of shallow roadway with weakly cemented soft strata

Based on a shallow roadway with weakly cemented soft strata in western China, this paper studies the range and degree of plastic zones in soft strata roadways with weak cementation. Geological radars were used to monitor the loose range and level of surrounding rocks. A mechanical model of weakly cemented roadway was established, including granular material based on the measured results. The model was then used to determine the plastic zone radium. The predicted results agree well with measured results which provide valuable theoretical references for the analysis of surrounding rock stability and support reinforcing design of weakly cemented roadways. Finally, a combined supporting scheme of whole section bolting and grouting was proposed based on the original supporting scheme. It is proved that this support plan can effectively control the deformation and plastic zone expansion of the roadway surrounding rock and thus ensure the long-term stable and safe mining.

Support vector regression model for complex target RCS predicting

The electromagnetic scattering computation has developed rapidly for many years; some computing problems for complex and coated targets cannot be solved by using the existing theory and computing models. A computing model based on data is established for making up the insufficiency of theoretic models. Based on the ＂support vector regression method＂, which is formulated on the principle of minimizing a structural risk, a data model to predicate the unknown radar cross section of some appointed targets is given. Comparison between the actual data and the results of this predicting model based on support vector regression method proved that the support vector regression method is workable and with a comparative precision.

果园移动机器人激光雷达双源信息融合实时导航方法

为提高林果园移动机器人导航系统的精确性与鲁棒性,提出一种基于激光雷达三维点云的果园行间高低频双源信息融合实时导航方法。首先,喷雾机器人搭载三维激光雷达采集两侧果树点云信息,对原始点云数据进行直通滤波、降采样和统计滤波等预处理,保留感兴趣区域内果树冠层点云;然后,将分别基于高频更新的牛顿插值算法和低频更新的非线性支持向量机(Non-linear support vector machine,NSVM)算法拟合的行间导航线进行互补融合;最后,在导航线切换时,对融合后导航线的稳定性进行优化,并使用三次B样条算法使导航线平滑。实验结果表明:融合优化后的导航线最大曲率为0.048 m^(-1),平均曲率为0.018 m^(-1);分别以0.5 m/s和1.0 m/s的行驶速度对融合优化后的导航线进行跟踪,绝对横向偏差最大值分别为0.104 m和0.130 m,平均值分别为0.053 m和0.049 m,说明该导航方法能够满足作业装备在果园行间自主导航作业的需求,为喷雾机器人在果园环境中的自主导航提供技术参考。

一种基于最大似然估计的宽带单脉冲测角改进方法

电大尺寸目标的宽带散射回波可看成多个强散射中心的共同作用结果,回波表现为高分辨距离像(high-resolution range profiles,HRRP)的特点。如何利用多个散射中心的回波能量,以提升单脉冲测角的性能是值得深入研究的问题。本文给出的宽带雷达单脉冲测角的最大似然估计(maximum likelihood estimate,MLE)算法,该方法能够积累扩散到多个距离单元的回波能量,从HRRP中获得信噪比(signal to noise ratio,SNR)增益。提出了一种基于回波本身来确定目标距离支集的方法,并在距离支集上实施MLE算法。仿真研究表明:本文所提方法相比于加权平均法和最强点法,能够有效利用距离方向多个散射点的回波能量。MLE算法的均方根误差(root mean square error,RMSE)性能逼近克拉美罗下界(Carmer Rao low bound,CRLB)。

联合SVM与AHP的PolSAR图像滑坡自动检测方法

针对复杂背景下单时相极化合成孔径雷达(PolSAR)图像滑坡泥石流毁损区检测正确率低、虚警多的问题,提出一种联合支持向量机(SVM)与层次分析法(AHP)的滑坡泥石流毁损区自动检测方法。首先,为充分利用极化信息,基于相干矩阵的九个元素构建特征向量并进行SVM地物分类,初步提取出区域分类结果;其次,基于归一化极化散射功率、同极化相关系数和雷达植被指数构建因素集合,并采用AHP方法优化评价因素权重,改善评分标准,更准确地判定滑坡泥石流毁损区的表面散射特性;最后,通过逻辑“与”运算将二者结果结合起来,实现了准确检测滑坡泥石流毁损区域并有效降低虚警的目的。利用2015年深圳地区和2018年日本北海道伊布里地区的ALOS-2/PALSAR-2数据进行了滑坡泥石流毁损区检测实验。实验结果表明,所提方法相比现有方法显著抑制林地、耕地等目标的虚警率,且在这两个滑坡案例中的滑坡目标检测正确率分别提升了约6%和2%。

基于SVM的捷变频雷达密集转发干扰智能抑制方法

密集转发干扰与雷达发射信号高度相关,兼具压制式和欺骗式干扰效果,使雷达系统难以检测到真实目标,严重威胁雷达作战能力。针对这一问题,该文提出一种基于支持向量机(SVM)的捷变频雷达密集转发干扰智能抑制方法。通过对随机样本集进行离线训练获得最优SVM模型,智能化识别并分类目标和干扰;然后,采用平滑滤波进一步抑制目标所在距离单元内的干扰信号;最后,基于压缩感知(CS)理论进行二维高分辨重构,估计出目标参数信息。仿真实验与实测数据处理结果表明,所提算法在不同场景下均能够有效抑制密集转发干扰,准确检测出真实目标。

高重频PD雷达导引头低空目标检测性能分析

文中以检测低空突防目标为背景,分析了在多径散射、海杂波和热噪声以及远距离支援干扰的情况下,高重频脉冲多普勒(PD)雷达导引头对低空突防目标的检测性能。首先,通过对多径条件下的目标回波信号、海杂波、热噪声以及远距离支援干扰进行建模,推导得出了高重频PD雷达接收信号的表达形式;然后,结合具体场景对雷达接收信号进行仿真,并以此综合分析了多径散射、海杂波、远距离支援干扰对目标回波的影响效应;最后,得到了不同干扰因素对PD雷达导引头检测性能的影响效果。